docs: def guess_depth_cutoff(cb_histogram):
      ''' Guesses at an appropriate barcode cutoff
      '''
      with read_cbhistogram(cb_histogram) as fh:
          cb_vals = [int(p.strip().split()[1]) for p in fh]
      histo = np.histogram(np.log10(cb_vals), bins=50)
      vals = histo[0]
      edges = histo[1]
      mids = np.array([(edges[i] + edges[i+1])/2 for i in range(edges.size - 1)])
      wdensity = vals * (10**mids) / sum(vals * (10**mids))
      baseline = np.median(wdensity)
      wdensity = list(wdensity)
      # find highest density in upper half of barcode distribution
      peak = wdensity.index(max(wdensity[len(wdensity)/2:]))
      cutoff = None
      for index, dens in reversed(list(enumerate(wdensity[1:peak]))):
          if dens < 2 * baseline:
              cutoff = index
              break
      if not cutoff:
          return None
      else:
          cutoff = 10**mids[cutoff]
          logger.info('Setting barcode cutoff to %d' % cutoff)
          return cutoff